Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA.
Center for Computational Oncology, Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas, USA.
Magn Reson Med. 2018 Jul;80(1):330-340. doi: 10.1002/mrm.26995. Epub 2017 Nov 8.
Quantitative evaluation of dynamic contrast enhanced MRI (DCE-MRI) allows for estimating perfusion, vessel permeability, and tissue volume fractions by fitting signal intensity curves to pharmacokinetic models. These compart mental models assume rapid equilibration of contrast agent within each voxel. However, there is increasing evidence that this assumption is violated for small molecular weight gadolinium chelates. To evaluate the error introduced by this invalid assumption, we simulated DCE-MRI experiments with volume fractions computed from entire histological tumor cross-sections obtained from murine studies.
A 2D finite element model of a diffusion-compensated Tofts-Kety model was developed to simulate dynamic T signal intensity data. Digitized histology slices were segmented into vascular (v ), cellular and extravascular extracellular (v ) volume fractions. Within this domain, K (the volume transfer constant) was assigned values from 0 to 0.5 min . A representative signal enhancement curve was then calculated for each imaging voxel and the resulting simulated DCE-MRI data analyzed by the extended Tofts-Kety model.
Results indicated parameterization errors of -19.1% ± 10.6% in K , -4.92% ± 3.86% in v , and 79.5% ± 16.8% in v for use of Gd-DTPA over 4 tumor domains.
These results indicate a need for revising the standard model of DCE-MRI to incorporate a correction for slow diffusion of contrast agent. Magn Reson Med 80:330-340, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
定量评估动态对比增强磁共振成像(DCE-MRI)可以通过将信号强度曲线拟合到药代动力学模型来估计灌注、血管通透性和组织体积分数。这些隔间模型假设对比剂在每个体素内迅速达到平衡。然而,越来越多的证据表明,对于小分子钆螯合物,这种假设是不成立的。为了评估这种无效假设引入的误差,我们使用从鼠类研究中获得的整个组织肿瘤横截面计算的体积分数模拟了 DCE-MRI 实验。
开发了一个二维有限元模型,用于模拟扩散补偿的 Tofts-Kety 模型的动态 T 信号强度数据。数字化的组织切片被分割为血管(v)、细胞和细胞外细胞外(v)体积分数。在这个域内,将 K(体积转移常数)分配的值从 0 到 0.5 分钟。然后为每个成像体素计算一个代表性的信号增强曲线,并通过扩展的 Tofts-Kety 模型分析由此产生的模拟 DCE-MRI 数据。
结果表明,在使用 Gd-DTPA 时,K 的参数化误差为-19.1%±10.6%,v 的参数化误差为-4.92%±3.86%,v 的参数化误差为 79.5%±16.8%,用于 4 个肿瘤域。
这些结果表明,需要修改 DCE-MRI 的标准模型,以纳入对比剂扩散缓慢的校正。磁共振医学 80:330-340,2018。© 2017 年国际磁共振学会。